Multimorbidity impacts cardiovascular disease risk following percutaneous coronary intervention: latent class analysis of the Melbourne Interventional Group (MIG) registry

Background Multimorbidity is strongly associated with disability or functional decline, poor quality of life and high consumption of health care services. This study aimed (1) To identify patterns of multimorbidity among patients undergoing first recorded percutaneous coronary intervention (PCI); (2) To explore the association between the identified patterns of multimorbidity on length of hospital stay, 30-day and 12- month risk of major adverse cardiac and cerebrovascular events (MACCE) after PCI. Methods A retrospective cohort study of the Melbourne Interventional Group (MIG) registry. This study included 14,025 participants who underwent their first PCI from 2005 to 2015 in Victoria, Australia. Based on a probabilistic modelling approach, Latent class analysis was adopted to classify clusters of people who shared similar combinations and magnitude of the comorbidity of interest. Logistic regression models were used to estimate odd ratios and 95% confidence interval (CI) for the 30-day and 12-month MACCE. Results More than two-thirds of patients had multimorbidity, with the most prevalent conditions being hypertension (59%) and dyslipidaemia (60%). Four distinctive multimorbidity clusters were identified each with significant associations for higher risk of 30-day and 12-month MACCE. The cluster B had the highest risk of 30-day MACCE event that was characterised by a high prevalence of reduced estimated glomerular filtration rate (92%), hypertension (73%) and reduced ejection fraction (EF) (57%). The cluster C, characterised by a high prevalence of hypertension (94%), dyslipidaemia (88%), reduced eGFR (87%), diabetes (73%) and reduced EF (65%) had the highest risk of 12-month MACCE and highest length of hospital stay. Conclusion Hypertension and dyslipidaemia are prevalent in at least four in ten patients undergoing coronary angioplasty. This study showed that clusters of patients with multimorbidity had significantly different risk of 30-day and 12-month MACCE after PCI. This suggests the necessity for treatment approaches that are more personalised and customised to enhance patient outcomes and the quality of care delivered to patients in various comorbidity clusters. These results should be validated in a prospective cohort and to evaluate the potential impacts of these clusters on the prevention of MACCE after PCI. Supplementary Information The online version contains supplementary material available at 10.1186/s12872-023-03636-7.


Background
More people are surviving acute coronary events largely attributed to advances in medical care and improved risk factor management.Consequently, more people are living with two or more chronic diseases; so called multimorbidity [1][2][3][4][5].In Australia, nearly half of patients presenting at General Practices and one third of the general population report multimorbidity [6].A recent study by Lai et al. 2019 observed a progressively increased rate of hospital admission in middle aged participants with multimorbidity as compared to those at older age [7].Multimorbidity is strongly associated with disability or functional decline, poor quality of life and high consumption of health care services [2,8,9].
Growing epidemiological research examining the prevalence of multimorbidity has mostly focused on investigating the number of co-existing conditions or there dyad/triad combinations [10,11].In a UK study, three multimorbidity phenotypic clusters (low, medium and high levels of multimorbidity) was identified among patients with acute myocardial infarction (AMI) and found a dose-response relationship between the extent of multimorbidity and long-term survival [12].This study used both cluster and latent class analyses that enable the distinction of co-morbid conditions that occur by chance from those that are statistically more likely to co-exist due to pathophysiological causes and/or socioeconomic or behavioural factors [13,14].However, the UK study restricted to AMI patients with single endpoint of allcause mortality.Recently, patterns of multimorbidity was considered given the potential overlapping pathophysiological pathways [15] and thereby inform the allocation of health services [16,17].A study by Islam et al. further reported that it is not the number, but the patterns of multimorbidity that predict health service use [18].Using a similar latent class analysis (LCA) method, this current study aimed to identify (1) patterns of multimorbidity among patients undergoing first recorded percutaneous coronary investigation (PCI) (both emergency and elective procedures); (2) the association between the identified patterns of multimorbidity on length of hospital stay, 30-day and 12-month risk of major adverse cardiac and cerebrovascular events (MACCE) after PCI.

Study design, settings and population
We conducted a retrospective cohort study of the Melbourne Interventional Group (MIG) registry [19].The MIG registry included 14,025 participants who underwent their first PCI from 2005 to 2015 at six tertiary referral hospitals in Victoria, Australia.The registry recorded data on patients' demographic characteristics, medical conditions at admission; PCI procedure; admission presentation, length of hospital stay, 30-day and 12-month MACCE at post procedure date.In this study, MACCE was defined deaths, non-fatal myocardial infarction, stroke and target vessel revascularisation.Follow-up information at 30-day and 12-month after PCI was collected in a standardised case report forms via telephone interview or record review by site nurse coordinators.

Statistical analyses
Based on a probabilistic modelling approach, LCA was adopted to classify clusters of people who shared similar combinations and magnitude of the comorbidity of interest.The comorbidity of interest included: (1) hypertension, (2) dyslipidaemia, (3) diabetes mellitus, (4) chronic lung disease (CLD), (5) obstructive sleep apnoea (OSA), (6) reduced ejection fraction (EF < 45%), (7) peripheral vascular disease (PVD), (8) cerebrovascular disease, and (9) reduced estimated glomerular filtration rate (eGFR < 60 ml/min/1.73m2).Each morbidity in the model is classified into one of the latent classes according to their maximum likelihood class membership [20].LCA incorporates standard procedures to handle missing values before assigning estimated probabilities of an individual belonging to distinct clusters [20].Model selection was based on model fit parameters including likelihoodratio G2 statistic, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and entropy.
Descriptive analyses were used to summarise the characteristics of patients according to comorbidity clusters identified by LCA.Differences between the comorbidity clusters groups for baseline characteristics were tested by t-tests for continuous variables and Chi-squared tests for categorical variables.Logistic regression models were used to estimate odd ratios and 95% confidence interval (CI) for the 30-day and 12-month MACCE.Both logistic and negative binomial regression models were performed for the association between comorbidity clusters and length of hospital stay.The models were adjusted for age, gender and other risk factors.The statistical significance was determined with a two-sided p-value below 0.05.Data analyses were performed using STATA (version 14).
customised to enhance patient outcomes and the quality of care delivered to patients in various comorbidity clusters.These results should be validated in a prospective cohort and to evaluate the potential impacts of these clusters on the prevention of MACCE after PCI.Keywords Multimorbidity, Cardiovascular Disease, Latent class analysis

Results
In Table 1, more than half of the included patients were males (74%) and had a mean age of 63 ± 12 years.In the PCI cohort, hypertension and dyslipidaemia were the most prevalent conditions (59%); followed by diabetes, reduced EF and reduced eGFR in approximately 20% and the remaining conditions accounted for proportions < 10%.Indications for PCI included 78% of patients presented with acute coronary syndrome (ACS), of which 40% were ST elevated myocardial infarction (STEMI).4% of the cohort had cardiogenic shock.Most PCI procedures were classified as urgent or rescue (77%) and 26% were multi-lesion procedures.
Missing records were minimal except for records of EF (10.4%) and New York Heart Association (NYHA) class (12.8%) as presented in Supplementary Table 1.The prevalence of comorbid conditions remained stable over time, except for a decrease in dyslipidaemia and impaired EF and a slight increase in CLD in more recent years (Supplementary Table 2).

Association between identified comorbidity clusters and the outcomes
After PCI, the average, hospital stay was four days; 7% (993 events) experienced MACCE after 30 days and 13% (1857 events) at 12 months.In Table 2, compared to cluster A (as the reference group), odds ratios (ORs) of having longer length of hospital stay was statistically higher in cluster D, B and C with adjusted OR 1.03 (95%CI 1.01-1.05),OR 1.32 (95%CI 1.27-1.36)and OR 1.71 (95%CI 1.64-1.78)respectively.Patients in cluster B and D had at least twice odds of 30-day MACCE events than those in

Discussion
In this study, over two-thirds of included patients had multimorbidity as defined, with the most prevalent conditions being hypertension and dyslipidaemia.Four distinctive multimorbidity clusters were associated with significant high risk of 30-day and 12-month MACCE and length of hospital stay.Clusters B and C shared high proportions of reduced eGFR, reduced EF and hypertension but with a low proportion of diabetes in cluster B (7%).Yet, the study by Hall et al [12] using the similar LCA analysis identified only three multimorbidity clusters: [1] the high risk class with high proportion of heart failure, PVD and hypertension, [2] the medium risk class with PVD and hypertension and [3] the low risk class with low levels of interested multimorbidity with less than 5% except for high prevalence of PVD of over 20%.Although morbidities were similar in both studies the differences may be explained by how the comorbidity of interest was defined in two studies.The Hall et al. study looked at a diagnosis of chronic renal failure (defined as serum creatinine level above 2.26 mg/dl) or chronic heart failure whereas our study collected data of reduced eGFR < 60 ml/min/1.73m2and reduced EF < 45%.Also, the Hall et al. study restricted to patients with ACS only.In our study, reduced eGFR and reduced EF were likely to coexist and related to poor cardiovascular outcomes due to synergetic effects of traditional and uremia-related risk factors [21].Patients with impaired kidney function tend to be under treated with preventive medicines upon discharge such as aspirin and statins [22][23][24] due to reticence relating to safety and benefits of these preventive medicines [25,26].There is a need of studies to confirm the effects of additional medicines to manage competing morbidities designed to improve the outcome after PCI in these patients [27].Also, previous studies showed a lack of referral to cardiac rehabilitation in patients with multimorbidity [28,29] even though they obtained similar benefits to those without multimorbidity [30][31][32].
Our study data was based on the MIG registry that has broad comparability to all Australian PCI patients and thus the results of our study are generalisable [33].Compared to other data sources, clinical quality registries provide reliable, consistent and clinically relevant data.This study adopted LCA that is a data-driven approach to identify groups of patients according to the probabilities of the existence/co-existence of different conditions [34].Using this method, we are able to understand the patterns and probabilities of multimorbidity among PCI patients in real world.In addition, LCA includes procedures for handling missing values, and therefore it can be applied to incomplete data [20].However, a key limitation of this study is the limited number of conditions recorded in the MIG registry.We did not have information on a number of relevant conditions, namely, anaemia, mental disorders, musculoskeletal disorders and obesity.Thus, our study is likely to have underestimated the association between multimorbidity and cardiac outcomes among PCI patients.Nevertheless, the nine morbidity conditions recorded in the MIG registry and thus included in the analysis are the most relevant ones to the prevention and progression of CHD.Another limitation was that the quality and accuracy of data collection determined the interval validity of the analysis.The recording of dyslipidaemia in the dataset was based on patients' prior diagnosis of dyslipidaemia or use of statins.Therefore, it did not reflect the lipid levels at admission.Yet, the number/ proportion of missing records were low, especially the recording of multimorbidity.Thirdly, our study included historical data from 2005 to 2015, during which there may have been changes in clinical practice and patient's presentations for PCI.However, we found no clinically significant differences over time with regard to patient's demographic characteristics nor the prevalence of the nine conditions (Supplementary file).

Conclusion
The combination of hypertension and dyslipidaemia is prevalent in at least four in ten patients undergoing coronary angioplasty.This study showed that clusters of patients with multimorbidity had significantly different risk of 30-day and 12-month MACCE after PCI.This suggests the necessity for treatment approaches that are more personalised and customised to enhance patient outcomes and the quality of care delivered to patients in various comorbidity clusters.These results should be validated in a prospective cohort and to evaluate the potential impacts of these clusters on the prevention of MACCE after PCI.